ks.exp.test: Kolmogorov-Smirnov test for exponentiality

In exptest: Tests for Exponentiality

Description

Performs Kolmogorov-Smirnov test for the composite hypothesis of exponentiality, see e.g. Henze and Meintanis (2005, Sec. 2.1).

Usage

ks.exp.test(x, nrepl=2000)

Arguments

x	a numeric vector of data values.

nrepl

the number of replications in Monte Carlo simulation.

Details

The Kolmogorov-Smirnov test for exponentiality is based on the following statistic:

KS_n =\sup_{x≥q0}|F_n(x)-(1-\exp(-x))|,

where F_n is the empirical distribution function of the scaled data Y_j=X_j/\overline{X}. The p-value is computed by Monte Carlo simulation.

Value

A list with class "htest" containing the following components:

statistic	the value of the Kolmogorov-Smirnov statistic.
p.value	the p-value for the test.
method	the character string "Kolmogorov-Smirnov test for exponentiality".
data.name	a character string giving the name(s) of the data.

Author(s)

Ruslan Pusev and Maxim Yakovlev

References

Henze, N. and Meintanis, S.G. (2005): Recent and classical tests for exponentiality: a partial review with comparisons. — Metrika, vol. 61, pp. 29–45.

Examples

ks.exp.test(rexp(100))
ks.exp.test(runif(100, min = 50, max = 100))

Example output

	Kolmogorov-Smirnov test for exponentiality

data:  rexp(100)
KSn = 0.073457, p-value = 0.408


	Kolmogorov-Smirnov test for exponentiality

data:  runif(100, min = 50, max = 100)
KSn = 0.50138, p-value < 2.2e-16

exptest documentation built on May 1, 2019, 8:01 p.m.